To design a reliable evaluation metric for acoustic style transfer,
we propose a pair of evaluation criterion to generate diverse data for the purpose of data augmentation.
One is built on the classification accuracy on the transferred data,
another one relies on the similarity between the transferred data and the input content and target style.
The classification performance on the transferred data includes the prediction accuracy on the content,
and the accuracy on the style. The generated data is expected to beat the classifier that trained from
dataset without these transferred instances included.
Style transfer can introduce diverse environmental scenes to the training set.
Once our model can accurately recognize these transferred data,
it will be more robust and have a better generalization
than the one trained from the content or style only audios.
Another objective is maximize content preservation. The input semantic content, if well-preserved
in the generated data, will keep complete in structure.
For speech and music style transfer, the content must be strictly preserved,
otherwise the neural transfer model
probably generates some utter meaningless sounds[1]}.
